class_docstr: An internal YOLO model wrapper that tracks metrics, and logs models to Weights & Biases.
  
  Usage:
  ```python
  from wandb.integration.yolov8.yolov8 import WandbCallback
  
  model = YOLO("yolov8n.pt")
  wandb_logger = WandbCallback(
      model,
  )
  for event, callback_fn in wandb_logger.callbacks.items():
      model.add_callback(event, callback_fn)
  ```
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docstr: Property contains all the relevant callbacks to add to the YOLO model for the Weights & Biases logging.
docstr_tok: ["Property","contains","all","the","relevant","callbacks","to","add","to","the","YOLO","model","for","the","Weights","&","Biases","logging","."]
code: def callbacks(
      self,
  ) -> Dict[str, Callable]:
      """Property contains all the relevant callbacks to add to the YOLO model for the Weights & Biases logging."""
      return {
          "on_pretrain_routine_start": self.on_pretrain_routine_start,
          "on_pretrain_routine_end": self.on_pretrain_routine_end,
          "on_train_epoch_start": self.on_train_epoch_start,
          "on_train_epoch_end": self.on_train_epoch_end,
          "on_fit_epoch_end": self.on_fit_epoch_end,
          "on_train_end": self.on_train_end,
          "on_model_save": self.on_model_save,
          "teardown": self.teardown,
      }
  
file_path: wandb/wandb/integration/yolov8/yolov8.py
imports: from typing import Any, Callable, Dict, List, Optional
  from ultralytics.yolo.engine.model import YOLO
  from ultralytics.yolo.engine.trainer import BaseTrainer
  from ultralytics.yolo.v8.classify.train import ClassificationTrainer
  import wandb
  from wandb.sdk.lib import telemetry
label_desc: Use image node_id 10 for calling the WandbCallback obj's underlying member method code with example usage: obj.callbacks() and returns: dict